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Non-Rigid Registration Based Segmentation of Brain Subcortical Structures Using a Priori Knowledge

机译:基于非刚性登记基于先验知识的脑皮质结构的分割

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Segmentation of the brain internal structures is an important and a challenging task due to their complex shapes, partial volume effects, low contrasts and anatomical variability between subjects. In this paper we propose a new non-rigid registration method that automatically segments the deep brain internal structures from brain MRI images. An atlas of the structures is used as a priori knowledge, which is modeled as a shape representation. By integrating the shape knowledge into a classical intensity based non-rigid registration algorithm, the proposed segmentation method allows to ameliorate the results in the case of low contrast on the boundaries of the structures. The shape model is based on distance representation obtained from the atlas. The segmentation of brain subcortical structures is performed on real MRI images and the obtained results are very encouraging.
机译:由于其复杂的形状,部分体积效应,低对比度,低对比度和解剖学,脑内部结构的分割是一个重要的和具有挑战性的任务。在本文中,我们提出了一种新的非刚性登记方法,可以自动分离来自脑MRI图像的深脑内部结构。结构的图谱用作先验知识,其被建模为形状表示。通过将形状知识集成到基于经典强度的非刚性登记算法中,所提出的分段方法允许改善结构的低对比度的情况下的结果。形状模型基于从地图集获得的距离表示。脑下脑面机结构的分割是对真正的MRI图像进行的,并且获得的结果非常令人鼓舞。

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